我在尝试使用训练好的网络进行预测时遇到了这个错误：无法从层’fc6’复制参数0的权重
这是我的deploy.prototxt文件
name: "CaffeNet"layer { name: "data" type: "Input" top: "data" input_param { shape: { dim: 1 dim: 1 dim: 150 dim: 227 } }}layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" convolution_param { num_output: 96 kernel_size: 11 stride: 4 }}layer { name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1"}layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 3 stride: 2 }}layer { name: "norm1" type: "LRN" bottom: "pool1" top: "norm1" lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 }}layer { name: "conv2" type: "Convolution" bottom: "norm1" top: "conv2" convolution_param { num_output: 256 pad: 2 kernel_size: 5 group: 2 }}layer { name: "relu2" type: "ReLU" bottom: "conv2" top: "conv2"}layer { name: "pool2" type: "Pooling" bottom: "conv2" top: "pool2" pooling_param { pool: MAX kernel_size: 3 stride: 2 }}layer { name: "norm2" type: "LRN" bottom: "pool2" top: "norm2" lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 }}layer { name: "conv3" type: "Convolution" bottom: "norm2" top: "conv3" convolution_param { num_output: 384 pad: 1 kernel_size: 3 }}layer { name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3"}layer { name: "conv4" type: "Convolution" bottom: "conv3" top: "conv4" convolution_param { num_output: 384 pad: 1 kernel_size: 3 group: 2 }}layer { name: "relu4" type: "ReLU" bottom: "conv4" top: "conv4"}layer { name: "conv5" type: "Convolution" bottom: "conv4" top: "conv5" convolution_param { num_output: 256 pad: 1 kernel_size: 3 group: 2 }}layer { name: "relu5" type: "ReLU" bottom: "conv5" top: "conv5"}layer { name: "pool5" type: "Pooling" bottom: "conv5" top: "pool5" pooling_param { pool: MAX kernel_size: 3 stride: 2 }}layer { name: "fc6" type: "InnerProduct" bottom: "pool5" top: "fc6" inner_product_param { num_output: 4096 }}layer { name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6"}layer { name: "drop6" type: "Dropout" bottom: "fc6" top: "fc6" dropout_param { dropout_ratio: 0.5 }}layer { name: "fc7" type: "InnerProduct" bottom: "fc6" top: "fc7" inner_product_param { num_output: 4096 }}layer { name: "relu7" type: "ReLU" bottom: "fc7" top: "fc7"}layer { name: "drop7" type: "Dropout" bottom: "fc7" top: "fc7" dropout_param { dropout_ratio: 0.5 }}layer { name: "fc8" type: "InnerProduct" bottom: "fc7" top: "fc8" inner_product_param { num_output: 2 }}layer { name: "prob" type: "Softmax" bottom: "fc8" top: "prob"}这是我的train_val.prototxt文件
name: "CaffeNet"layer { name: "data" type: "Data" top: "data" top: "label" include { phase: TRAIN } transform_param { mirror: true crop_size: 150 mean_file: "/home/ttb010/TT/partituras/input/mean.binaryproto" }# mean pixel / channel-wise mean instead of mean image# transform_param {# crop_size: 150# mean_value: 104# mean_value: 117# mean_value: 123# mirror: true# } data_param { source: "/home/ttb010/TT/partituras/input/train_lmdb" batch_size: 256 backend: LMDB }}layer { name: "data" type: "Data" top: "data" top: "label" include { phase: TEST } transform_param { mirror: false crop_size: 150 mean_file: "/home/ttb010/TT/partituras/input/mean.binaryproto" }# mean pixel / channel-wise mean instead of mean image# transform_param {# crop_size: 150# mean_value: 104# mean_value: 117# mean_value: 123# mirror: true# } data_param { source: "/home/ttb010/TT/partituras/input/validation_lmdb" batch_size: 50 backend: LMDB }}layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 11 stride: 4 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } }}layer { name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1"}layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 3 stride: 2 }}layer { name: "norm1" type: "LRN" bottom: "pool1" top: "norm1" lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 }}layer { name: "conv2" type: "Convolution" bottom: "norm1" top: "conv2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 2 kernel_size: 5 group: 2 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 1 } }}layer { name: "relu2" type: "ReLU" bottom: "conv2" top: "conv2"}layer { name: "pool2" type: "Pooling" bottom: "conv2" top: "pool2" pooling_param { pool: MAX kernel_size: 3 stride: 2 }}layer { name: "norm2" type: "LRN" bottom: "pool2" top: "norm2" lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 }}layer { name: "conv3" type: "Convolution" bottom: "norm2" top: "conv3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 384 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } }}layer { name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3"}layer { name: "conv4" type: "Convolution" bottom: "conv3" top: "conv4" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 384 pad: 1 kernel_size: 3 group: 2 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 1 } }}layer { name: "relu4" type: "ReLU" bottom: "conv4" top: "conv4"}layer { name: "conv5" type: "Convolution" bottom: "conv4" top: "conv5" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 group: 2 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 1 } }}layer { name: "relu5" type: "ReLU" bottom: "conv5" top: "conv5"}layer { name: "pool5" type: "Pooling" bottom: "conv5" top: "pool5" pooling_param { pool: MAX kernel_size: 3 stride: 2 }}layer { name: "fc6" type: "InnerProduct" bottom: "pool5" top: "fc6" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 4096 weight_filler { type: "gaussian" std: 0.005 } bias_filler { type: "constant" value: 1 } }}layer { name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6"}layer { name: "drop6" type: "Dropout" bottom: "fc6" top: "fc6" dropout_param { dropout_ratio: 0.5 }}layer { name: "fc7" type: "InnerProduct" bottom: "fc6" top: "fc7" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 4096 weight_filler { type: "gaussian" std: 0.005 } bias_filler { type: "constant" value: 1 } }}layer { name: "relu7" type: "ReLU" bottom: "fc7" top: "fc7"}layer { name: "drop7" type: "Dropout" bottom: "fc7" top: "fc7" dropout_param { dropout_ratio: 0.5 }}layer { name: "fc8" type: "InnerProduct" bottom: "fc7" top: "fc8" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 15 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } }}layer { name: "accuracy" type: "Accuracy" bottom: "fc8" bottom: "label" top: "accuracy" include { phase: TEST }}layer { name: "loss" type: "SoftmaxWithLoss" bottom: "fc8" bottom: "label" top: "loss"}我一直在搜索，但我不完全理解为什么会发生这种情况。我使用的是灰度图像，然后对它们进行二值化。我在训练网络时使用的输入图像大小为150×227。我知道在deploy.prototxt中，输入参数形状维度代表使用的通道数和图像大小，但我缺少另一个维度。解决这个问题的可能方法是什么？在此之前非常感谢，我很绝望。
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